Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolving Neural Control Systems
IEEE Expert: Intelligent Systems and Their Applications
Evolving neural networks through augmenting topologies
Evolutionary Computation
Solving Non-Markovian Control Tasks with Neuro-Evolution
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Human-Level AI's Killer Application: Interactive Computer Games
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Neuroevolution of an automobile crash warning system
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A comparison between cellular encoding and direct encoding for genetic neural networks
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
Connectionist theory refinement: genetically searching the space of network topologies
Journal of Artificial Intelligence Research
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
Synthesizing neural networks for learning in games
Future Play '08 Proceedings of the 2008 Conference on Future Play: Research, Play, Share
Evolving multi-modal behavior in NPCs
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Computationally efficient behaviour based controller for real time car racing simulation
Expert Systems with Applications: An International Journal
Evolving patch-based terrains for use in video games
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Hi-index | 0.00 |
A major goal for AI is to allow users to interact with agents that learn in real time, making new kinds of interactive simulations, training applications, and digital entertainment possible. This paper describes such a learning technology, called real-time NeuroEvolution of Augmenting Topologies (rtNEAT), and describes how rtNEAT was used to build the NeuroEvolving Robotic Operatives (NERO) video game. This game represents a new genre of machine learning games where the player trains agents in real time to perform challenging tasks in a virtual environment. Providing laymen the capability to effectively train agents in real time with no prior knowledge of AI or machine learning has broad implications, both in promoting the field of AI and making its achievements accessible to the public at large.